Conventional ultrasound imaging has been widely applied in various domains such as industry, military, and medicine etc. When the ultrasound imaging system is applied in medical applications, it can be used to measure physiological characteristics of human tissues such as flow rates of blood in blood vessels, etc. Ultrasound energy is transmitted to a region of human tissue to be detected, and ultrasound energy reflected by said region is received. According to the reflected ultrasound energy, an ultrasound imaging system can display a two-dimensional ultrasound image of the region to be detected. However, regarding the detection of low flow rates of the blood in blood vessels or the diameters of smaller blood vessels, specific signal analysis methods are required to analyse the ultrasound energy reflected by the region to be detected, so as to obtain the related information.
According to the conventional signal analysis methods, the reflected ultrasound energy is analyzed in frequency domain. However, such frequency domain analysis methods have some shortcomings. In detail, according to the Doppler principle, when the ultrasound imaging system sends the ultrasound to a moving object in the region to be detected, for example, an erythrocyte in the blood vessel, a frequency of an echo signal reflected by the moving object is shifted, and the frequency shift is proportional to a component of the speed of the moving object along an ultrasound transmission direction. In a pulsed wave Doppler mode, a probe of the ultrasound imaging system sends a series of short pulses to the region to be detected, and the reflected signals received by the ultrasound imaging system can be represented as a two-dimensional data set, where one dimension represents a pulse sending index (i.e. a slow time axis), and another dimension represents a flying time (i.e. a fast time axis). The signals on the slow time axis carry the Doppler shift information. Therefore, the characteristic information of the moving object can be obtained by analyzing the phases of the signals on the slow time axis.
The conventional signal analysis method generally calculates a self-correlation function of two adjacent signals on slow time axis to estimate a phase shift, and accordingly calculates information of the moving object such as the speed, etc. However, according to the conventional signal analysis method, the signal on the slow time axis is processed by Fourier transform, that is, the signal analysis is performed in frequency domain. However, the conventional signal analysis method has following shortcomings. The Doppler signal tends to be interfered by low-frequency signals, for example, pulses of a vessel wall, heart pulses, breathing or involuntary movements. Such method has to use a high-pass filter (a wall filter) to filter the noises, so as to perform the subsequent analysis. It is not easy to design an ideal high-pass filter, so the complexity of the frequency domain analysis method is increased. The Fourier transform that serves as a core algorithm of frequency domain analysis method is essentially an integral transform, and once time domain signals are transformed, all of time-varying information are completely lost. Therefore, the conventional signal analysis method cannot provide specific instantaneous frequency information in an actual measurement.